Recent Projects

As a Microsoft Gold Partner, Imaginet is committed to staying on top of the latest Microsoft technologies. Read just some of our recent projects we've worked on for organizations just like yours.

Increased Report and Analytics Efficiency

USA’s #1 grower of vegetable and herb plants needed to gather data from Microsoft Dynamics and improve speed and efficiency when accessing reports. Previously, they refreshed large amounts of data on a daily basis to feed QlikView reports resulting in delays and stalls. To increase analytics efficiency, Imaginet designed and built a data warehouse hosted in Azure to integrate operational data. Multiple pipelines in Azure Data Factory were created to fetch data from various domains like finance, inventory, production, and payroll. Data were sourced from different on-premise SQL Server databases then loaded into their data warehouse. SQL scripts were developed to create data factory task queries, merge stored procedures, and views to accommodate the columns for the dimensional model. Lastly, Imaginet deployed a data model to Power BI that allowed them to get instant insights into their production and create their own reports and visualizations in a proof of concept workspace. DevOps pipelines were created to accommodate deployments for different environments.

Technologies:

  • Microsoft BI Stack
    • Azure Data Factory
    • Power BI
  • Microsoft Languages
    • DAX
    • T-SQL
    • RDBMS
    • SQL Server 2016
    • Azure SQL Server
  • Other Tools
    • Microsoft Dynamics
    • QlikView

Gained Holistic and Accurate View of Mortgage Data

A nationally recognized mortgage lender needed to analyze and report on mortgage data on a daily basis. However, data were stored in multiple disparate systems making it difficult to get an accurate, holistic view of their data. Encompass (an information management system) contained loan information, Velocify managed lead information., and Nextiva had historical data related to calls. In order to gain clarity and make better business decisions, data needed to be extracted across systems and loaded into a data warehouse to be useable in Power BI. Imaginet built an ETL (extract, transform, load) process and multiple pipelines in Azure Data Factory to fetch data from HTTP endpoints and raw files (csv / xml) to the source system. We then transformed the data in a relational form and loaded it into their data warehouse. SQL scripts were developed to create data factory task queries, merge stored procedures, and views to accommodate the columns for the dimensional model. Lastly, we developed a Power BI model sourcing data from the data warehouse allowing them to visualize and analyze data from a single source of truth, and create highly accurate and visually stunning reports and dashboards.

Technologies:

  • Microsoft BI Stack
    • Azure Data Factory
    • Power BI
  • Microsoft Languages
    • T-SQL
    • DAX
    • DBMS
    • Azure SQL Server
    • Snowflake
    • Rest APIs

Modernized Data Storage and Reporting

A crown corporation engaged Imaginet to design a storage structure of data from a retiring IBM UniData system, extract the data, prepare it for storage, replicate the ODBC connection functionality in SQL Server and Excel, replicate reports in a reporting tool, and develop trust in the data. Data was extracted to a SQL Server database using SSIS pipelines and C# custom applications. SQL objects were created to prepare the database as a source for analytics tools. Reports were built using Microsoft SSRS and ASP.NET. The data was also provided to power users through an Excel connection.

Technologies:

  • Microsoft BI Stack
    • SSIS
    • SSRS
  • Microsoft Languages
    • C#
    • ASP.NET
    • T-SQL

WellView Peloton API Extraction

An oil and gas subsidiary commissioned Imaginet to extract data from their WellView information management system into a data warehouse to be used with other data consumers. A custom C# app was developed in order to extract data periodically from WellView using Peloton Rest APIs and store the output data to JSON files in a server machine. A pipeline in Azure Data Factory was created to fetch data from the files, transform them in a relational form, and load raw data to their SQL data warehouse. Some SQL development was done to create new tables and views to select all required table columns. Finally, a WellView SSIS package was incorporated into a current SSIS project to load WellView data to join with other database tables. Views, packages, and tables were added or updated to support a data mart. The existing tabular model was updated with the new data so users can consume the data from Excel.

Technologies:

  • Microsoft BI Stack
    • SSIS
    • Azure Data Factory
    • SSAS
  • Microsoft Languages
    • C#
    • T-SQL
    • RDBMS
    • SQL Server 2016
    • REST APIs

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